Bias reduction in parameter estimation
type: Item
instance of: scholarly article
title: Bias reduction in parameter estimation
zbMATH Open document ID: 0658.93075
DOI: 10.1016/0005-1098(88)90059-3
author: S. H. Smith
published in: Automatica
publication date: 1988-01-01T00:00:00Z
review text: The upper bound of the bias in the estimated parameters is derived in terms of the spectral norm. It is then established that the bias is related to the condition of the problem as well as the noise existing in the data. Conventional methods such as the generalized least squares (GLDS), the instrumental variables (IV) and the extended matrix (EM) method seek to minimize bias by reducing the level of noise correlation in the data. The method presented in this paper approaches the bias reduction problem via the condition number, which acts as a multiply factor on the bias due to noise correlation. By improving the condition of the problem, it can be shown that bias in the estimated parameters is significantly reduced.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 62M15
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 93C05
Mathematics Subject Classification ID: 93C55
zbMATH DE Number: 4077104
zbMATH Keywords: bias
zbMATH Keywords: spectral norm
zbMATH Keywords: generalized least squares
zbMATH Keywords: instrumental variables
zbMATH Keywords: extended matrix
zbMATH Keywords: condition number
MaRDI profile type: Publication
full work available at URL: 0005-1098(88)90059-3
OpenAlex ID: W2037989916
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description: scientific article; zbMATH DE number 4077104